Abstract: Traditional pest control methods require a lot of manpower and material resources, and cannot achieve high accuracy. In order to do a more scientific and efficient pest control work, this paper combines deep learning technology and Internet of Things technology to develop a pest detection system for remote pest detection and improve the efficiency of control work. The system mainly adopts the YOLO-v5 network model combined with transfer learning to train and learn the characteristics of common forest pests and farmland pests, so as to achieve efficient detection and identification. Based on the internet of things technology, remote control is achieved to capture images of pests and diseases, and they are transmitted to the computer for recognition through Wi Fi, and through the visual interface to show the types and quantities of pests in the field, to reduce the consumption of manpower and material resources and scientific pest control has important practical value.